63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
import numpy as np
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# from pytorch_lightning import seed_everything
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from .annotator.util import resize_image, HWC3
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from .annotator.openpose import OpenposeDetector
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from PIL import Image
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import torch
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class OpenPose:
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def __init__(self, body_model_path: str, *, device):
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self.preprocessor = OpenposeDetector(body_model_path, device=device)
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def __call__(self, input_image, resolution=384):
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if isinstance(input_image, Image.Image):
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input_image = np.asarray(input_image)
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elif type(input_image) == str:
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input_image = np.asarray(Image.open(input_image))
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else:
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raise ValueError
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with torch.no_grad():
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input_image = HWC3(input_image)
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input_image = resize_image(input_image, resolution)
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H, W, C = input_image.shape
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assert (H == 512 and W == 384), 'Incorrect input image shape'
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pose, detected_map = self.preprocessor(input_image, hand_and_face=False)
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candidate = pose['bodies']['candidate']
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subset = pose['bodies']['subset'][0][:18]
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for i in range(18):
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if subset[i] == -1:
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candidate.insert(i, [0, 0])
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for j in range(i, 18):
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if(subset[j]) != -1:
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subset[j] += 1
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elif subset[i] != i:
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candidate.pop(i)
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for j in range(i, 18):
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if(subset[j]) != -1:
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subset[j] -= 1
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candidate = candidate[:18]
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for i in range(18):
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candidate[i][0] *= 384
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candidate[i][1] *= 512
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keypoints = {"pose_keypoints_2d": candidate}
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# with open("/home/aigc/ProjectVTON/OpenPose/keypoints/keypoints.json", "w") as f:
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# json.dump(keypoints, f)
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#
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# # print(candidate)
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# output_image = cv2.resize(cv2.cvtColor(detected_map, cv2.COLOR_BGR2RGB), (768, 1024))
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# cv2.imwrite('/home/aigc/ProjectVTON/OpenPose/keypoints/out_pose.jpg', output_image)
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return keypoints
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if __name__ == '__main__':
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model = OpenPose()
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model('./images/bad_model.jpg')
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